Tao Cheng (程涛), Ph.D.

Professor

Department of Smart Agriculture, College of Smart Agriculture & College of Agriculture

Associate Director, National Engineering and Technology Center for Information Agriculture

Nanjing Agricultural University

One Weigang, Nanjing 210095, Jiangsu, China

Phone: (+86) 25 8439 9791   E-mail: tcheng@njau.edu.cn

http://web.netcia.org.cn/ TaoCheng.html (in English)

https://www.researchgate.net/profile/Tao_Cheng2  (in English)

http://web.netcia.org.cn/ TaoCheng_cn.html (in Chinese)

Last update: December 29, 2025

 

EDUCATION

Ph.D. in Earth & Atmospheric Sciences, September 2010

University of Alberta, Edmonton, Alberta, Canada

 

M. Eng. in Photogrammetry & Remote Sensing, July 2006

Peking University, Beijing, China

 

B. Sc. in Geographic Information System, June 2003

Lanzhou University, Lanzhou, Gansu, China

 

PROFESSIONAL EXPERIENCE

12/2013 – present      Professor, Nanjing Agricultural University, Nanjing, China

1/2011 – 12/2013       Postdoctoral scholar, University of California, Davis, California, USA

9/2006 – 12/2010        Research/teaching assistant, University of Alberta, Edmonton, Alberta, Canada

 

CAUSES TAUGHT

Remote Sensing for Agricultural Applications: Principles and Techniques (Excellent English-taught course accredited by the Ministry of Education and the Provincial Education Department of Jiangsu)

Cases of Agricultural Engineering and Information Technology

Introduction to Smart Agriculture Development

Information Agriculture Technologies

Introduction to Geospatial Technologies

Professional English in Crop Science

Undergraduate Seminar

Undergraduate Seminar

 

 

RESEARCH INTERESTS

§     Radiative transfer and geometric optical modeling in crops

§     Remote sensing of foliar chemistry

§     Crop type and management mapping

§     Crop growth monitoring

§     Crop yield and quality prediction

§     Crop disease detection

§     Crop phenotyping

§     Precision farming

 

PUBLICATIONS

ResearcherID: http://www.researcherid.com/rid/B-4807-2010

Google Scholar: http://scholar.google.com/citations?user=uOGtKrcAAAAJ

 

Peer-reviewed papers in English (* denotes corresponding author):

1.        Xue, B., Kong, Y., Zarco-Tejada, P.J., Tian, L., Poblete, T., Wang, X., Zheng, H., Jiang, C., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2026). Mitigating the phenological influence on spectroscopic quantification of rice blast disease severity with extended PROSAIL simulations. Remote Sensing of Environment, 332, 115063.

2.      Yu, W., Xiong, Y., Li, X., Zheng, H., Jiang, C., Yao, X., Zhu, Y.*, Cao, W., Qiu, L., Song, L., & Cheng, T.* (2026). Rice yield prediction in unseen years at field level with high-resolution gross primary productivity derived from Sentinel-2 imagery. Remote Sensing of Environment, 332, 115061.

3.        Li, D., Belwalkar, A., Cheng, T., & Yu, K. (2026). PAGrid: A probabilistic area-weighted gridding method for seamless mapping of sentinel-3 swath data. Remote Sensing of Environment, 333, 115165.

4.        Yang, S., Belwalkar, A., Li, D., Ge, Y., Cheng, T., Wu, F., Peng, L., Li, D., & Yu, K. (2025). DeepSpecN: A new hybrid method combining PROSPECT-PRO and Conv-Transformer to estimate leaf nitrogen content from leaf reflectance. Plant Phenomics, 7, 100125.

5.        Tian, L., Ustin, S.L., Xue, B., Zarco-Tejada, P.J., Jin, Y., Yao, X., Zhu, Y.*, Cao, W., & Cheng, T.* (2025). Visualizing the pre-visual: Rice blast infection signals revealed. Remote Sensing of Environment, 328, 114905.

6.      Sun, H., Wei, Z., Yu, W., Yang, G., She, J., Zheng, H., Jiang, C., Yao, X., Zhu, Y., Cao, W., Cheng, T.*, & Ali, I. (2025). SIDEST: A sample-free framework for crop field boundary delineation by integrating super-resolution image reconstruction and dual edge-corrected Segment Anything model. Computers and Electronics in Agriculture, 230, 109897. (Download the crop field dataset at https://doi.org/10.5281/zenodo.14257921 and the software at https://doi.org/10.5281/zenodo.14263411.)

7.        Xue, B., Tian, L., Zarco-Tejada, P.J., Kong, Y., Poblete, T., Wang, X., Zheng, H., Jiang, C., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2025). A two-step approach to mitigating phenological influences on spectroscopic detection of rice blast and removing pseudo severity estimates in healthy plants. Computers and Electronics in Agriculture, 236, 110458.

8.        Yan, Y., Li, D., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2025). Integration of multiscale spectral features as the intermediate variables for Improved prediction of grain protein concentration in winter wheat. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-18.

9.        Yang, G., Li, X., Xiong, Y., He, M., Zhang, L., Jiang, C., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2025). Annual winter wheat mapping for unveiling spatiotemporal patterns in China with a knowledge-guided approach and multi-source datasets. ISPRS Journal of Photogrammetry and Remote Sensing, 225, 163-179. (Download the ChinaWheat30L product at https://zenodo.org/records/15124014.)

10.    Wu, Y., Yu, W., Gu, Y., Zhang, Q., Xiong, Y., Zheng, H., Jiang, C., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2025). Accurate estimation of grain number per panicle in winter wheat by synergistic use of UAV imagery and meteorological data. International Journal of Applied Earth Observation and Geoinformation, 136, 104320.

11.    Li, D., Chen, J.M., Yu, W., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2025). Corrigendum to “A chlorophyll-constrained semi-empirical model for estimating leaf area index using a red-edge vegetation index” [Comput. Electron. Agric. 220 (2024) 108891]. Computers and Electronics in Agriculture, 229, 109722.

12.    Li, W., Li, D., Warner, T.A., Liu, S., Baret, F., Yang, P., Jiang, J., Dong, M., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2025). Improved generality of wheat green LAI models through mitigation of the effect of leaf chlorophyll content variation with red edge vegetation indices. Remote Sensing of Environment, 318

13.    Li, D., Croft, H., Duveiller, G., Schreiner-McGraw, A.P., Belwalkar, A., Cheng, T., Zhu, Y., Cao, W., & Yu, K. (2025). Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models. Remote Sensing of Environment, 328

14.    Zhou, M., Zhu, J., Ai, H., Zhang, Y., Warner, T.A., Zheng, H., Jiang, C., Cheng, T., Tian, Y., Zhu, Y., Cao, W., & Yao, X. (2025). A in-seasonal phenology monitoring approach for wheat breeding accessions with time-series RGB imagery by using a combination KNN-CNN-RF model. ISPRS Journal of Photogrammetry and Remote Sensing, 227, 297-315

15.    Guo, T., Li, W., Disney, M., Wang, Y., Zheng, H., Zhou, D., Jiang, C., Tian, Y., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2025). Assessing Sampling Design and Voxel Size in Estimating Wheat Green Area Index With Measured and Simulated TLS Data. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-14

16.    Guo, T., Wang, Y., Gu, Y., Fang, Y., Zheng, H., Zhang, X., Zhou, D., Jiang, C., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2025). MSCVI: An improved algorithm for mitigating LiDAR noise and occlusion effects in field wheat tiller number calculation. Computers and Electronics in Agriculture, 229

17.    Wang, Y., Shao, M., Wang, J., An, J., Wu, J., Yao, X., Zhang, X., Jiang, C., Cheng, T., Tian, Y., Cao, W., Zhou, D., & Zhu, Y. (2025). SMICGS: A novel snapshot multispectral imaging sensor for quantitative monitoring of crop growth. Plant Phenomics, 7, 100056

18.    Yu, W., Li, D., Zheng, H., Yao, X., Zhu, Y., Cao, W., Qiu, L., Cheng, T.*, Zhang, Y., & Zhou, Y. (2024). HIDYM: A high-resolution gross primary productivity and dynamic harvest index based crop yield mapper. Remote Sensing of Environment, 311, 114301.

19.    Yang, G., Li, X., Xiong, Y., He, M., Wang, X., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2024). Winter wheat mapping without ground labels via automated knowledge transfer across regions and years. Computers and Electronics in Agriculture, 227, 109536.

20.    Li, D., Chen, J.M., Yu, W., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2024). A chlorophyll-constrained semi-empirical model for estimating leaf area index using a red-edge vegetation index. Computers and Electronics in Agriculture, 220, 108891.

21.    Li, D., Wu, Y., Berger, K., Kuang, Q., Feng, W., Chen, J.M., Wang, W., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2024). Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model. International Journal of Applied Earth Observation and Geoinformation, 135, 104280.

22.    Li, D., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2024). Assessing the sensitivity of semiempirical models to spectral data quality and sensor settings when estimating leaf chlorophyll content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 4062-4070.

23.    Gu, Y., Wang, Y., Wu, Y., Warner, T.A., Guo, T., Ai, H., Zheng, H., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2024). Novel 3D photosynthetic traits derived from the fusion of UAV LiDAR point cloud and multispectral imagery in wheat. Remote Sensing of Environment, 311

24.    Gu, Y., Wang, Y., Guo, T., Guo, C., Wang, X., Jiang, C., Cheng, T., Zhu, Y., Cao, W., Chen, Q., & Yao, X. (2024). Assessment of the influence of UAV-borne LiDAR scan angle and flight altitude on the estimation of wheat structural metrics with different leaf angle distributions. Computers and Electronics in Agriculture, 220

25.    Mustafa, G., Zheng, H., Khan, I.H., Zhu, J., Yang, T., Wang, A., Xue, B., He, C., Jia, H., Li, G., Cheng, T., Cao, W., Zhu, Y., & Yao, X. (2024). Enhancing fusarium head blight detection in wheat crops using hyperspectral indices and machine learning classifiers. Computers and Electronics in Agriculture, 218

26.    Mustafa, G., Zheng, H., Liu, Y., Yang, S., Khan, I.H., Hussain, S., Liu, J., Weize, W., Chen, M., Cheng, T., Zhu, Y., & Yao, X. (2024). Leveraging machine learning to discriminate wheat scab infection levels through hyperspectral reflectance and feature selection methods. European Journal of Agronomy, 161

27.    Han, X., Zhou, M., Guo, C., Ai, H., Li, T., Li, W., Zhang, X., Chen, Q., Jiang, C., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2024). A fully convolutional neural network model combined with a Hough transform to extract crop breeding field plots from UAV images. International Journal of Applied Earth Observation and Geoinformation, 132

28.    Li, W., He, J., Yu, M., Su, X., Wang, X., Zheng, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). Multisource Remote Sensing Data-Driven Estimation of Rice Grain Starch Accumulation: Leveraging Matter Accumulation and Translocation Characteristics. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-18

29.    Pan, Y., Wu, W., He, J., Zhu, J., Su, X., Li, W., Li, D., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). A novel approach for estimating fractional cover of crops by correcting angular effect using radiative transfer models and UAV multi-angular spectral data. Computers and Electronics in Agriculture, 222

30.    Su, X., He, J., Li, W., Pan, Y., Li, D., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). Monitoring Rice Leaf Nitrogen Content Based on the Canopy Structure Effect Corrected With a Novel Model PROSPECT-P. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-17

31.    Tang, Y., Pan, Y., Zhao, Y., Li, X., He, J., Guo, C., Zheng, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). Estimating wheat partitioning coefficient using remote sensing and its coupling with a crop growth model. Field Crops Research, 319

32.    Tao, H., Zhou, R., Tang, Y., Li, W., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). Estimating wheat spike-leaf composite indicator (SLI) dynamics by coupling spectral indices and machine learning. The Crop Journal

33.    Wang, Y., Gu, Y., Tang, J., Guo, B., Warner, T.A., Guo, C., Zheng, H., Hosoi, F., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2024). Quantify Wheat Canopy Leaf Angle Distribution Using Terrestrial Laser Scanning Data. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15

34.    Yu, M., He, J., Li, W., Zheng, H., Wang, X., Yao, X., Cheng, T., Zhang, X., Zhu, Y., Cao, W., & Tian, Y. (2024). Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs). Remote Sensing, 16, 2073

35.    Yuan, J., Li, X., Zhou, M., Zheng, H., Liu, Z., Liu, Y., Wen, M., Cheng, T., Cao, W., Zhu, Y., & Yao, X. (2024). Rapidly count crop seedling emergence based on waveform Method(WM) using drone imagery at the early stage. Computers and Electronics in Agriculture, 220

36.    Zhao, Y., He, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events. Remote Sensing, 16

37.    Zhao, Y., Xiao, L., Tang, Y., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). Spatio-temporal change of winter wheat yield and its quantitative responses to compound frost-dry events – An example of the Huang-Huai-Hai Plain of China from 2001 to 2020. Science of The Total Environment, 940

38.    Zhao, Y., Zhang, Z., Tang, Y., Guo, C., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2024). Improving the estimation accuracy of wheat maturity date by coupling WheatGrow with satellite images. European Journal of Agronomy, 160

39.    Zheng, H., Tang, W., Yang, T., Zhou, M., Guo, C., Cheng, T., Cao, W., Zhu, Y., Zhang, Y., & Yao, X. (2024). Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study. Plant Phenomics, 6, 0200

40.    Tian, L., Wang, Z., Xue, B., Li, D., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2023). A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space. Remote Sensing of Environment, 285, 113384.

41.    Yang, G., Li, X., Liu, P., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2023). Automated in-season mapping of winter wheat in China with training data generation and model transfer. ISPRS Journal of Photogrammetry and Remote Sensing, 202, 422-438. (Download the ChinaWheat10 product at https://doi.org/10.5281/zenodo.8119065.)

42.    Yan, Y., Li, D., Kuang, Q., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2023). Integration of canopy water removal and spectral triangle index for improved estimations of leaf nitrogen and grain protein concentrations in winter wheat. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-18.

43.   Xue, B., Tian, L., Wang, Z., Wang, X., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2023). Quantification of rice spikelet rot disease severity at organ scale with proximal imaging spectroscopy. Precision Agriculture, 24, 1049-1071.

44.    Zhou, M., Zheng, H., He, C., Liu, P., Awan, G.M., Wang, X., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2023). Wheat phenology detection with the methodology of classification based on the time-series UAV images. Field Crops Research, 292, 108798.

45.    Yin, Y., Zhu, J., Xu, X., Jia, M., Warner, T.A., Wang, X., Li, T., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2023). Tracing the nitrogen nutrient status of crop based on solar-induced chlorophyll fluorescence. European Journal of Agronomy, 149, 126924.

46.    Tang, Y., Zhou, R., He, P., Yu, M., Zheng, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2023). Estimating wheat grain yield by assimilating phenology and LAI with the WheatGrow model based on theoretical uncertainty of remotely sensed observation. Agricultural and Forest Meteorology, 339, 109574.

47.    Su, X., Wang, J., Ding, L., Lu, J., Zhang, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2023). Grain yield prediction using multi-temporal UAV-based multispectral vegetation indices and endmember abundance in rice. Field Crops Research, 299, 108992.

48.    Pan, Y., Wu, W., Zhang, J., Zhao, Y., Zhang, J., Gu, Y., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2023). Estimating leaf nitrogen and chlorophyll content in wheat by correcting canopy structure effect through multi-angular remote sensing. Computers and Electronics in Agriculture, 208, 107769.

49.    Ma, Z., Li, W., Warner, T.A., He, C., Wang, X., Zhang, Y., Guo, C., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2023). A framework combined stacking ensemble algorithm to classify crop in complex agricultural landscape of high altitude regions with Gaofen-6 imagery and elevation data. International Journal of Applied Earth Observation and Geoinformation, 122, 103386.

50.    Li, Y., Zeng, H., Zhang, M., Wu, B., Zhao, Y., Yao, X., Cheng, T., Qin, X., & Wu, F. (2023). A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering. International Journal of Applied Earth Observation and Geoinformation, 118, 103269.

51.    Li, W., Li, D., Liu, S., Baret, F., Ma, Z., He, C., Warner, T.A., Guo, C., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2023). RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background. ISPRS Journal of Photogrammetry and Remote Sensing, 200, 138-152.

52.    Li, D., Chen, J.M., Yu, W., Zheng, H., Yao, X., Cao, W., Wei, D., Xiao, C., Zhu, Y.*, & Cheng, T.* (2022). Assessing a soil-removed semi-empirical model for estimating leaf chlorophyll content. Remote Sensing of Environment, 282, 113284.

53.    Li, D., Chen, J.M., Yan, Y., Zheng, H., Yao, X., Zhu, Y., Cao, W.*, & Cheng, T.* (2022). Estimating leaf nitrogen content by coupling a nitrogen allocation model with canopy reflectance. Remote Sensing of Environment, 283, 113314.

54.    Jiang, J., Zhang, Q., Wang, W., Wu, Y., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2022). MACA: A relative radiometric correction method for multiflight unmanned aerial vehicle images based on concurrent satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14.

55.    Lu, N., Wu, Y., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2022). An assessment of multi-view spectral information from UAV-based color-infrared images for improved estimation of nitrogen nutrition status in winter wheat. Precision Agriculture, 23, 1653-1674.

56.    Wang, W., Zheng, H., Wu, Y., Yao, X., Zhu, Y., Cao, W., & Cheng, T.* (2022). An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation times. Field Crops Research, 283, 108543.

57.    Zheng, H., Ji, W., Wang, W., Lu, J., Li, D., Guo, C., Yao, X., Tian, Y., Cao, W., Zhu, Y., & Cheng, T.* (2022). Transferability of models for predicting rice grain yield from unmanned aerial vehicle (UAV) multispectral imagery across years, cultivars and sensors. Drones, 6, 423.

58.    Mustafa, G., Zheng, H., Khan, I.H., Tian, L., Jia, H., Li, G., Cheng, T., Tian, Y., Cao, W., Zhu, Y., & Yao, X. (2022). Hyperspectral reflectance proxies to diagnose in-field fusarium head blight in wheat with machine learning. Remote Sensing, 14, 2784.

59.    Li, X., Ata-Ui-Karim, S.T., Li, Y., Yuan, F., Miao, Y., Yoichiro, K., Cheng, T., Tang, L., Tian, X., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2022). Advances in the estimations and applications of critical nitrogen dilution curve and nitrogen nutrition index of major cereal crops. A review. Computers and Electronics in Agriculture, 197, 106998.

60.    Jiang, J., Liu, H., Zhao, C., He, C., Ma, J., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2022). Evaluation of diverse convolutional neural networks and training strategies for wheat leaf disease identification with field-acquired photographs. Remote Sensing, 14, 3446.

61.    He, J., Ma, J., Cao, Q., Wang, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2022). Development of critical nitrogen dilution curves for different leaf layers within the rice canopy. European Journal of Agronomy, 132, 126414.

62.    Tian, L., Xue, B., Wang, Z., Li, D., Yao, X., Cao, Q., Zhu, Y., Cao, W., & Cheng, T.* (2021). Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection. Remote Sensing of Environment, 257, 112350.

63.    Yang, G., Yu, W., Yao, X., Zheng, H., Cao, Q., Zhu, Y., Cao, W., & Cheng, T.* (2021). AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 102, 102446.

64.    Wang, W., Wu, Y., Zhang, Q., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T. (2021). AAVI: A novel approach to estimating leaf nitrogen concentration in rice from unmanned aerial vehicle multispectral imagery at early and middle growth stages. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6716-6728.

65.    Alebele, Y., Wang, W., Yu, W., Zhang, X., Yao, X., Tian, Y., Zhu, Y., Cao, W., & Cheng, T.* (2021). Estimation of crop yield from combined optical and sar imagery using gaussian kernel regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10520-10534.

66.    Yan, Y., Zhang, X., Li, D., Zheng, H., Yao, X., Zhu, Y., Cao, W., & Cheng, T*. (2021). Laboratory shortwave infrared reflectance spectroscopy for estimating grain protein content in rice and wheat. International Journal of Remote Sensing, 42, 4467-4492.

67.    Zhang, X., Yang, G., Xu, X., Yao, X., Zheng, H., Zhu, Y., Cao, W., & Cheng, T.* (2021). An assessment of Planet satellite imagery for county-wide mapping of rice planting areas in Jiangsu Province, China with one-class classification approaches. International Journal of Remote Sensing, 42, 7610-7635.

68.    Yang, T., Lu, J., Liao, F., Qi, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Retrieving potassium levels in wheat blades using normalised spectra. International Journal of Applied Earth Observation and Geoinformation, 102, 102412.

69.    Lu, J., Eitel, J.U.H., Jennewein, J.S., Zhu, J., Zheng, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Combining remote sensing and meteorological data for improved rice plant potassium content estimation. Remote Sensing, 13, 3502.

70.    Lu, J., Eitel, J.U.H., Engels, M., Zhu, J., Ma, Y., Liao, F., Zheng, H., Wang, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Improving Unmanned Aerial Vehicle (UAV) remote sensing of rice plant potassium accumulation by fusing spectral and textural information. International Journal of Applied Earth Observation and Geoinformation, 104, 102592.

71.    Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., Cao, W., & Yao, X. (2021). Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral Iimages in wheat. Remote Sensing, 13, 3612.

72.    Jiang, J., Zhu, J., Wang, X., Cheng, T., Tian, Y., Zhu, Y., Cao, W., & Yao, X. (2021). Estimating the leaf nitrogen content with a new feature extracted from the ultra-high spectral and spatial resolution images in wheat. Remote Sensing, 13, 739.

73.    Jia, M., Colombo, R., Rossini, M., Celesti, M., Zhu, J., Cogliati, S., Cheng, T., Tian, Y., Zhu, Y., Cao, W., & Yao, X. (2021). Estimation of leaf nitrogen content and photosynthetic nitrogen use efficiency in wheat using sun-induced chlorophyll fluorescence at the leaf and canopy scales. European Journal of Agronomy, 122, 126192.

74.    Lu, J., Li, W., Yu, M., Zhang, X., Ma, Y., Su, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precision Agriculture, 22, 51-74.

75.    Cheng, T., Ji, X., Yang, G., Zheng, H., Ma, J., Yao, X., Zhu, Y., & Cao, W.* (2020). DESTIN: A new method for delineating the boundaries of crop fields by fusing spatial and temporal information from WorldView and Planet satellite imagery. Computers and Electronics in Agriculture, 178, 105787.

76.    Li, D., Chen, J.M., Zhang, X., Yan, Y., Zhu, J., Zheng, H., Zhou, K., Yao, X., Tian, Y., Zhu, Y., Cheng, T.*, & Cao, W.* (2020). Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time. Remote Sensing of Environment, 248, 111985.

77.    Li, P., Zhang, X., Wang, W., Zheng, H., Yao, X., Tian, Y., Zhu, Y., Cao, W., Chen, Q., & Cheng, T.* (2020). Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 91, 102132.

78.    Jiang, J., Zhang, Q., Yao, X., Tian, Y., Zhu, Y., Cao, W., & Cheng, T.* (2020). HISTIF: A new spatiotemporal image fusion method for high-resolution monitoring of crops at the subfield level. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4607-4626.

79.    Alebele, Y., Zhang, X., Wang, W., Yang, G., Yao, X., Zheng, H., Zhu, Y., Cao, W., & Cheng, T.* (2020). Estimation of canopy biomass components in paddy rice from combined optical and SAR data using multi-target Gaussian regressor stacking. Remote Sensing, 12, 2564.

80.    Fang, Y., Qiu, X., Guo, T., Wang, Y., Cheng, T., Zhu, Y., Chen, Q., Cao, W., Yao, X., Niu, Q., Hu, Y., & Gui, L. (2020). An automatic method for counting wheat tiller number in the field with terrestrial LiDAR. Plant Methods, 16, 132.

81.    He, J., Zhang, X., Guo, W., Pan, Y., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2020). Estimation of vertical leaf nitrogen distribution within a rice canopy based on hyperspectral data. Frontiers in Plant Science, 10, 1802.

82.    Zheng, H., Ma, J., Zhou, M., Li, D., Yao, X., Cao, W., Zhu, Y., & Cheng, T. (2020). Enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from unmanned aerial vehicle (UAV) multispectral imagery. Remote Sensing, 12, 957.

83.    Zheng, H., Zhou, X., He, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2020). Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV). Computers and Electronics in Agriculture, 169, 105223.

84.    Zhou, M., Ma, X., Wang, K., Cheng, T., Tian, Y., Wang, J., Zhu, Y., Hu, Y., Niu, Q., Gui, L., Yue, C., & Yao, X. (2020). Detection of phenology using an improved shape model on time-series vegetation index in wheat. Computers and Electronics in Agriculture, 173, 105398.

85.    Lu, N., Wang, W., Zhang, Q., Li, D., Yao, X., Tian, Y., Zhu, Y., Cao, W., Baret, F., Liu, S., & Cheng, T.* (2019). Estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi-angular multispectral imagery. Frontiers in Plant Science, 10, 1601.

86.    Li, D., Tian, L., Wan, Z., Jia, M., Yao, X., Tian, Y., Zhu, Y., Cao, W.*, & Cheng, T.* (2019). Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra. Remote Sensing of Environment, 231, 111240.

87.    Lu, N., Zhou, J., Han, Z., Li, D., Cao, Q., Yao, X., Tian, Y., Zhu, Y., Cao, W., & Cheng, T.* (2019). Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods, 15:17. (WoS Highly Cited Paper)

88.    Zheng, H., Cheng, T., Zhou, M., Li, D., Yao, X., Tian, Y., Cao, W., & Zhu, Y. (2019). Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precision Agriculture, 20, 611-629. (WoS Highly Cited Paper)

89.    Guo, T., Fang, Y., Cheng, T., Tian, Y., Zhu, Y., Chen, Q., Qiu, X., & Yao, X. (2019). Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages. Computers and Electronics in Agriculture, 165, 104959.

90.    Jiang, J., Zheng, H., Ji, X., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Ehsani, R., & Yao, X. (2019). Analysis and evaluation of the image preprocessing process of a six-band multispectral camera mounted on an unmanned aerial vehicle for winter wheat monitoring. Sensors, 19, 747.

91.    Jiang, J., Cai, W., Zheng, H., Cheng, T., Tian, Y., Zhu, Y., Ehsani, R., Hu, Y., Niu, Q., Gui, L., & Yao, X. (2019). Using digital cameras on an unmanned aerial vehicle to derive optimum color vegetation indices for leaf nitrogen concentration monitoring in winter wheat. Remote Sensing, 11, 2667.

92.    Cao, Z., Yao, X., Liu, H., Liu, B., Cheng, T., Tian, Y., Cao, W., & Zhu, Y. (2019). Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat. Agricultural and Forest Meteorology, 265, 121-136.

93.    Jia, M., Li, W., Wang, K., Zhou, C., Cheng, T., Tian, Y., Zhu, Y., Cao, W., & Yao, X. (2019). A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat. Computers and Electronics in Agriculture, 165, 104942.

94.    Jia, M., Li, D., Colombo, R., Wang, Y., Wang, X., Cheng, T., Zhu, Y., Yao, X., Xu, C., Ouer, G., Li, H., & Zhang, C. (2019). Quantifying chlorophyll fluorescence parameters from hyperspectral reflectance at the leaf scale under various nitrogen treatment regimes in winter wheat. Remote Sensing, 11, 2838.

95.    Li, S., Yuan, F., Ata-Ui-Karim, S.T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sensing, 11, 1763.

96.    He, J., Zhang, N., Su, X., Lu, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2019). Estimating leaf area index with a new vegetation index considering the influence of rice panicles. Remote Sensing, 11, 1809.

97.    Lu, J., Yang, T., Su, X., Qi, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2019). Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves. Precision Agriculture, 21, 324-348.

98.    Xu, X.Q., Lu, J.S., Zhang, N., Yang, T.C., He, J.Y., Yao, X., Cheng, T., Zhu, Y., Cao, W.X., & Tian, Y.C. (2019). Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 185-196.

99.    Guo, C., Tang, Y., Lu, J., Zhu, Y., Cao, W., Cheng, T., Zhang, L., & Tian, Y. (2019). Predicting wheat productivity: Integrating time series of vegetation indices into crop modeling via sequential assimilation. Agricultural and Forest Meteorology, 272-273, 69-80.

100.       Li, W., Jiang, J., Guo, T., Zhou, M., Tang, Y., Wang, Y., Zhang, Y., Cheng, T., Zhu, Y., Cao, W., & Yao, X. (2019). Generating red-edge images at 3 m spatial resolution by fusing Sentinel-2 and Planet satellite products. Remote Sensing, 11, 1422.

101.Bauer, A., Bostrom, A.G., Ball, J., Applegate, C., Cheng, T., Laycock, S., Rojas, S.M., Kirwan, J., & Zhou, J. (2019). Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production. Horticulture Research, 6, 70.

102.Li, D., Cheng, T.*, Jia, M., Zhou, K., Lu, N., Yao, X., Tian, Y., Zhu, Y., & Cao, W. (2018). PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra. Remote Sensing of Environment, 206, 1-14.

103.Li, D., Wang, X., Zheng, H., Zhou, K., Yao, X., Tian, Y., Zhu, Y., Cao, W., & Cheng, T.* (2018). Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis. Plant Methods, 14, 76.

104.Xu, X., Ji, X., Jiang, J., Yao, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q., Yang, H., Shi, Z., & Cheng, T.* (2018). Evaluation of one-class support vector classification for mapping the paddy rice planting area in Jiangsu province of China from Landsat 8 OLI imagery. Remote Sensing, 10, 546.

105.Jiang, J., Ji, X., Yao, X., Tian, Y., Zhu, Y., Cao, W., & Cheng, T.* (2018). Evaluation of three techniques for correcting the spatial scaling bias of leaf area index. Remote Sensing, 10, 221.

106.Zhou, K., Cheng, T., Zhu, Y., Cao, W., Ustin, S.L., Zheng, H., Yao, X., & Tian, Y. (2018). Assessing the impact of spatial resolution on the estimation of leaf nitrogen concentration over the full season of paddy rice using near-surface imaging spectroscopy data. Frontiers in Plant Science, 9:964.

107.Zheng, H., Cheng, T., Li, D., Yao, X., Tian, Y., Cao, W., & Zhu, Y. (2018). Combining unmanned aerial vehicle (UAV)-based multispectral imagery and ground-based hyperspectral data for plant nitrogen concentration estimation in rice. Frontiers in Plant Science, 9, 936.

108.Zheng, H., Cheng, T., Li, D., Zhou, X., Yao, X., Tian, Y., Cao, W., & Zhu, Y. (2018). Evaluation of RGB, color-infrared and multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sensing, 10, 824.

109.Zheng, H., Li, W., Jiang, J., Liu, Y., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Zhang, Y., & Yao, X. (2018). A comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sensing, 10, 2026.

110.Yao, X., Si, H., Cheng, T., Jia, M., Chen, Q., Tian, Y., Zhu, Y., Cao, W., Chen, C., Cai, J., & Gao, R. (2018). Hyperspectral estimation of canopy leaf biomass phenotype per ground area using a continuous wavelet analysis in wheat. Frontiers in Plant Science, 9, 1360.

111.Jia, M., Zhu, J., Ma, C., Alonso, L., Li, D., Cheng, T., Tian, Y., Zhu, Y., Yao, X., & Cao, W. (2018). Difference and potential of the upward and downward sun-induced chlorophyll fluorescence on detecting leaf nitrogen concentration in wheat. Remote Sensing, 10, 1315.

112.Li, S., Ding, X., Kuang, Q., Ata-UI-Karim, S.T., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2018). Potential of UAV-based active sensing for monitoring rice leaf nitrogen status. Frontiers in Plant Science, 9:1834.

113.Guo, C., Zhang, L., Zhou, X., Zhu, Y., Cao, W., Qiu, X., Cheng, T., & Tian, Y. (2018). Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning. Precision Agriculture, 19, 55-78.

114.Zhao, L., Xu, X., Zhang, M., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2018). Development and testing of an ear-leaf model for rice canopy reflectance. Journal of Applied Remote Sensing, 12, 016016.

115.Li, D., Cheng, T.*, Zhou, K., Zheng, H., Yao, X., Tian, Y., Zhu, Y., & Cao, W. (2017). WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops. ISPRS Journal of Photogrammetry and Remote Sensing, 129, 103-117.

116.Cheng, T., Song, R., Li, D., Zhou, K., Zheng, H., Yao, X., Tian, Y., Cao, W., & Zhu, Y. (2017). Spectroscopic estimation of biomass in canopy components of paddy rice using dry matter and chlorophyll indices. Remote Sensing, 9, 319.

117.Zhou, K., Deng, X., Yao, X., Tian, Y., Cao, W., Zhu, Y.*, Ustin, S.L., & Cheng, T.* (2017). Assessing the spectral properties of sunlit and shaded components in rice canopies with near-ground imaging spectroscopy data. Sensors, 17, 578.

118.Cao, Z., Cheng, T., Ma, X., Tian, Y., Zhu, Y., Yao, X., Chen, Q., Liu, S., Guo, Z., Zhen, Q., & Li, X. (2017). A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat. International Journal of Remote Sensing, 38, 3865-3885.

119.Yao, X., Wang, N., Liu, Y., Cheng, T., Tian, Y., Chen, Q., & Zhu, Y. (2017). Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sensing, 9, 1304.

120.Fang, M., Ju, W., Zhan, W., Cheng, T., Qiu, F., & Wang, J. (2017). A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves. Remote Sensing of Environment, 196, 13-27.

121.Zhou, X., Zheng, H.B., Xu, X.Q., He, J.Y., Ge, X.K., Yao, X., Cheng, T., Zhu, Y., Cao, W.X., & Tian, Y.C. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246-255. (WoS Highly Cited Paper)

122.Cheng, T., Yang, Z., Inoue, Y., Zhu, Y., & Cao, W. (2016). Preface: recent advances in remote sensing for crop growth monitoring. Remote Sensing, 8, 116. (Editorial for Special Issue “Recent Advances in Remote Sensing for Crop Growth Monitoring”)

123.Zheng, H., Cheng, T., Yao, X., Deng, X., Tian, Y., Cao, W., & Zhu, Y. (2016). Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Research, 198, 131-139.

124.Zhang, L., Guo, C.L., Zhao, L.Y., Zhu, Y., Cao, W.X., Tian, Y.C., Cheng, T., & Wang, X. (2016). Estimating wheat yield by integrating the WheatGrow and PROSAIL models. Field Crops Research, 192, 55-66.

125.Yao, X., Huang, Y., Shang, G., Zhou, C., Cheng, T., Tian, Y., Cao, W. & Zhu, Y. (2015). Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sensing, 7, 14939-14966.

126.Cheng, T., Riaño, D. & Ustin, S. L. (2014). Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sensing of Environment, 143, 39-53.

127.Cheng, T., Rivard, B., Sánchez-Azofeifa, G. A., Féret, J. B., Jacquemoud, S. & Ustin, S. L. (2014). Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 28-38.

128.Li, P., Yu, H., & Cheng, T. (2014). Lithologic mapping using ASTER imagery and multivariate texture. Canadian Journal of Remote Sensing, 35, S117-S125.

129.Chu, X., Guo, Y., He, J., Yao, X., Zhu, Y., Cao, W., Cheng, T. & Tian, Y. (2014). Comparison of different hyperspectral vegetation indices for estimating canopy leaf nitrogen accumulation in rice. Agronomy Journal, 106, 1911-1920.

130.Yao, X., Ren, H., Cao, Z., Tian, Y., Cao, W., Zhu, Y. & Cheng, T. (2014). Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds. International Journal of Applied Earth Observation and Geoinformation, 32, 114-124.

131.Cheng, T., Riaño, D., Koltunov, A., Whiting, M. L., Ustin, S. L. & Rodriguez, J. Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data. (2013). Remote Sensing of Environment, 132, 1-12.

132.Cheng, T., Rivard, B., Sánchez-Azofeifa, G. A., Féret, J. B., Jacquemoud, S. & Ustin, S. L. (2012). Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. Journal of Plant Physiology, 169, 1134-1142.

133.Jin, H., Li, P., Cheng, T. & Song, B. (2012). Land cover classification using CHRIS/PROBA images and multi-temporal texture. International Journal of Remote Sensing, 33,101-119.

134.Cheng, T., Rivard, B., & Sánchez-Azofeifa, G. A. (2011). Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment, 115, 659-670.

135.McKellar, R., Wolfe, A., Muehlenbachs, K., Tappert, R., Engel, M., Cheng, T., & Sánchez-Azofeifa, A. (2011). Insect outbreaks produce distinctive carbon isotope signatures in defensive resins and fossiliferous ambers. Proceedings of the Royal Society B: Biological Sciences. doi: 10.1098/rspb.2011.0276.

136.Cheng, T., Rivard, B., Sánchez-Azofeifa, G. A., Feng, J. & Calvo-Polanco, M. (2010). Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 114, 899-910.

137.Li, P., Cheng, T. & Guo, J. (2009). Multivariate image texture by multivariate variogram for multispectral image classification. Photogrammetric Engineering & Remote Sensing, 75, 147-157.

138.Li, P., Yu, H., & Cheng, T. (2009). Lithologic mapping using ASTER imagery and multivariate texture. Canadian Journal of Remote Sensing, 35, S117-S125.

 

Books and Chapters:

1.        Cheng, T., Zhu, Y., Li, D., Yao, X, & Zhou, K. (2018). Hyperspectral remote sensing of leaf nitrogen concentration in cereal crops. In P. S. Thenkabail, J. Lyon, & A. Huete (Eds.), Hyperspectral Remote Sensing of Vegetation, Second Edition, Four Volume Set, Volume 2. Boca Raton, FL: CRC Press.

2.        Cheng, T., Yang, Z., Inoue, Y., Zhu, Y., & Cao, W. (2016). Recent advances in remote sensing for crop growth monitoring (eds., p. 408). Basel: MDPI.

 

Conference presentations with proceedings:

1.        Tian, L., Wan, Z., Li, D., Jiang, J., Yao, X., Tian, Y., Zhu, Y., Cao, W., & Cheng, T*. Detecting rice blast using model inverted biochemical variables from close-range reflectance imagery of fresh leaves. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 22-27, 2018, Valencia, Spain.

2.        Cheng, T., Li, D., Zheng, H., Yao, X., Tian, Y., Zhu, Y., & Cao, W. Towards decomposing the effects of foliar nitrogen content and canopy structure on rice canopy spectral variability through multi-scale spectral analysis. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China, pp. 3508-3511. (Oral)

3.        Song, R., Cheng, T.*, Yao, X., Tian, Y., Zhu, Y., Cao, W. Evaluation of Landsat 8 time series image stacks for predicting yield and yield components of winter wheat. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China, pp. 6300-6303.

4.        Li, D., Cheng, T.*, Yao, X., Zhang, Z., Tian, Y., Zhu, Y., Cao, W. Wavelet-based PROSPECT inversion for retrieving leaf mass per area (LMA) and equivalent water thickness (EWT) from leaf reflectance. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China, pp. 6910-6913.

5.         Cheng, T., Li, D., Yao, X., Tian, Y., Zhu, Y. & Cao, W. A wavelet-based technique for extracting the red edge position from vegetation reflectance spectra. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 26-31, 2015, Milan, Italy, pp. 2673-2676. (Oral)

6.         Zhou, K., Tian, Y., Cheng, T., Yao, X., Zhu, Y. & Cao, W. Inversion of the PROSAIL model for extracting key vegetation biophysical parameter of wheat at canopy and regional levels. 3rd Agro-Geoinformatics, August 11-14, 2014, Beijing, China.

7.        Alsina, M. M., Cheng, T., Riaño, D., Whiting, M., Ustin, S. & Smart, D. Water status detection in California Table Grapes: from leaf to airborne. 9th European Conference on Precision Agriculture, July 7th-11th, 2013, Lleida, Catalonia, Spain.

8.        Ustin, S., Kefauver, S., Rodriguez, J., Cheng, T. & Riaño, D. Use of optical and thermal infrared imagery from AVIRIS/MASTER to estimate evapotranspiration. 2011 HyspIRI workshop, August 23-25, 2011, Washington, D.C.

9.        Cheng, T., Riaño, D., Koltunov, A., Whiting, M. L. &Ustin, S. L. (2011). Remote detection of water stress in orchard canopies using MODIS/ASTER airborne simulator (MASTER) data.Proceedings of SPIE 8156, August 21-25, 2011, San Diego, California. (Oral)

10.     Cheng, T., Rivard, B., & Sánchez-Azofeifa, G. A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 25-30, 2010, Honolulu, Hawaii. (Poster)

11.    Li, P., Cheng, T., Moser, G., Serpico, S.B. & Ma, D. (2007). Multitemporal change detection by spectral and multivariate texture information. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 23-27, 2007, Barcelona, Spain, pp. 1922-1925.

12.    Li, P., Cheng, T., Hu, H., & Xiao, X. (2006). High-resolution multispectral image classification over urban areas by image segmentation and extended morphological profile. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 31-August 4, 2006, Denver, Colorado, pp. 3252-3254.

13.    Li, P. & Cheng, T. (2005). Multitemporal image classification by multichannel texture and Support Vector Machines (SVM). Proceedings of the 9th International Symposium on Physical Measurements and Signature in Remote Sensing (ISPMSRS), October 17-19, 2005, Beijing, China, pp. 235-237.

14.     Cheng, T. & Li, P. (2005). Multivariate variogram-based multichannel image texture for image classification. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), July 25-29, 2005, Seoul, Korea, pp. 3830-3832. (Poster)

 

Conference presentations without proceedings:

1.        Cheng, T. Hyperspectral estimation of leaf chlorophyll and nitrogen contents in cereal crops with empirical and physical models. 1st Workshop on spaceborne hyperspectral applications. December 9, 2020. Nanning, China. (Invited)

2.        Cheng, T. Field-level agricultural monitoring for precision crop management. Workshop on Time series analysis of remotely sensed imagery for monitoring natural resources. November 15, 2020. Nanjing, China.

3.        Cheng, T. Li, D., Zheng, H., Lu, N., Yan, Y., Zhang, X., Yao, X., Tian, Y., Zhu, Y., & Cao, W. Crop nitrogen phenotyping from leaves to grains. 6th International Plant Phenotyping Symposium. October 22-26, 2019. Nanjing, China. (Invited)

4.        Cheng, T. Close-range imaging spectroscopy and its applications to crop growth monitoring. 2nd Zolix Workshop on Spectral Imaging. July 31, 2019. Nanjing, China (Invited)

5.        Cheng, T., Li, D., Yao, X., Tian, Y., Zhu, Y., & Cao, W. Continuous wavelet spectral analysis: a new methodology for the spectroscopic estimation of foliar chemistry. 4th Quantitative Remote Sensing Forum, June 14-16, 2019. Nanjing, China.

6.         Cheng, T. Is it feasible to invert the PROSPECT model with leaf clip measured reflectance spectra? Lica Workshop on Spectroscopy. May 28, 2019. Nanjing, China.

7.          Cheng, T. Spectral sensing of crop growth from leaf to regional levels. 2018 Joint Annual Meeting for Jiangsu Provincial Society of Remote Sensing & GIS and Jiangsu Provincial Society of Geography. December 8, 2018. Nanjing, China.

8.         Cheng, T. Multi-scale integrated growth monitoring for precision crop management: technologies and practices. 4th Sino-German Agricultural Week Forum on Smart Agriculture and Digital Rural Development, November 26, 2018. Beijing, China. (Invited)

9.         Cheng, T. PROCWT: a new algorithm for retrieving biochemistry from bidirectional reflectance spectra of leaves. Workshop on Vegetation Remote Sensing. October 27, 2018. Nanjing, China.

10.    Cheng, T. Continuous wavelet spectral analysis: a new approach for hyperspectral estimation of crop growth parameters. Conference on Remote Sensing of China. August 23, 2018. Deqing, China. (Invited)

11.    Cheng, T. Continuous wavelet spectral analysis for the quantification of crop chemistry. 1st Youth Agricultural Scientist Forum. April 22, 2018. Beijing, China. (Invited)

12.    Cheng, T., Li, D., Zhou, K., Zheng, H., Lu, N., Jia, M., Xu, X., Jiang, J., Yao, X., Tian, Y., Cao, W., & Zhu, Y. Multi-scale phenotyping for crop growth traits for precision cultivation. 2nd Asia-Pacific Plant Phenotyping Conference. March 23-25, 2018. Nanjing, China. (Keynote)

13.    Cheng, T., Baret, F., Liu, S., Lu, N., Li, D., Zhou, J., Franck, T., Jezequel, S., de Solan B., Comar, A., Yao, X., Tian, Y., Zhu, Y. & Han, D. Leaf chlorophyll content estimation from multispectral imagery acquired from unmanned aerial vehicle (UAV) over wheat crops. XIX International Botanical Congress. July 23-29, 2017, Shenzhen, China. (Oral)

14.    Cheng, T., Zheng, H., Lu, N., Zhou, J., Wang, N., Zhou, X., Yao, X., Tian, Y., Cao, W., & Zhu, Y. Field phenotyping with unmanned aerial vehicle (UAV) based remote sensing for crop cultivation purposes. 1st Asia-Pacific Plant Phenotyping Conference. October 19-21, 2016. Beijing, China. (Oral)

15.    Cheng, T. Understanding the water signals in leaf reflectance from a wavelet perspective. The 4th Ebernburg-Workshop “Leaf Optics”, October 14-16, 2015, Ebernburg, Germany. (Oral)

16.    Cheng, T., Zhou, K. Deng, X., Yao, X., Tian, Y., Zhu, Y., & Cao, W. Airborne and near-ground imaging spectroscopy for monitoring crop growth: advantages and challenges from spectral and spatial details. Joint International Conference on Intelligent Agriculture, September 27-29, 2015, Beijing, China. (Oral).

17.     Cheng, T., Song, R., Zheng, H., Deng, X., Zhou, X., Yao, X., Tian, Y., Zhu, Y. & Cao, W. Estimation of biomass for different canopy components of rice crops using chlorophyll and dry matter indices. International Conference on Carbon Cycle and Global Change, June 9-12, 2015, LinAn, Hangzhou, China. (Oral)

18.     Cheng, T., Riaño, D. & Ustin, S. L. Continuous wavelet analysis applied to imaging spectroscopy data for mapping canopy water content in agricultural vegetation. 35th International Symposium on Remote Sensing of Environment, April 22-26, 2013, Beijing, China. (Oral)

19.     Cheng, T., Riaño, D. & Ustin, S. L. Exploring the relationship between water flux and vegetation water status using time series data of evapotranspiration and MODIS vegetation indices. AGU 2012 Fall Meeting, December 3-7, 2012, San Francisco, CA. (Poster)

20.    Cheng, T., Riaño, D. & Ustin, S. L. Analysis of seasonal and diurnal variation in vegetation canopy water content using AVIRIS-derived liquid water products from ACORN. 2012 HyspIRI workshop, October 16-18, 2012, Washington, D. C. (Poster)

21.     Cheng, T., Rivard, B., Sánchez-Azofeifa, G. A., & Jacquemoud, S. Wavelets: a useful tool to derive vegetation properties from hyperspectral data. FLUXNET Workshop, June 7-9, 2011, Berkeley, California. (Poster)

22.    Cheng, T., Rivard, B., & Sánchez-Azofeifa, G. A. Identification of boreal tree species in northern Alberta with airborne hyperspectral imagery. GIS Day, November 15, 2007, University of Alberta, Edmonton, Alberta, Canada. (Poster)

 

INVITED PRESENTATIONS

l  Hyperspectral estimation of leaf chlorophyll and nitrogen contents in cereal crops with empirical and physical models. Zhejiang University. January 7, 2021. Hangzhou, China.

l  Hyperspectral estimation of leaf chlorophyll and nitrogen contents in cereal crops. Zolix Instruments Co., Ltd. November 26, 2020. Online.

l  Hyperspectral monitoring of leaf nitrogen and chlorophyll contents in cereal crops: explorations from leaf to global scales. Jiangsu Academy of Agricultural Sciences. October 27, 2020. Nanjing, China.

l  Hyperspectral estimation of crop nitrogen nutrition status related parameters. Peking University Summer School on Quantitative Remote Sensing. July 6, 2019. Beijing, China.

l  Benefits of the spatial and spectral details from ground-based hyperspectral imaging for crop monitoring. NAU Crop Phenomics Workshop. August, 2018, Nanjing, China.

l  Multi-scale remote sensing techniques for crop growth monitoring and acreage estimation. South China Agricultural University. June, 2018, Guangzhou, China.

l  Remote sensing for crop growth monitoring and planting area mapping. Institute of Crop Sciences, CAAS. May, 2018, Beijing, China.

l  Continuous wavelet spectral analysis (CWSA) for the spectroscopic estimation of foliar chemistry. National Physical Geography Conference 2017. November, 2017, Nanjing, China.

l  Hyperspectral remote sensing of foliar nitrogen content in cereal crops. Annual meeting of Jiangsu Society of RS & GIS. November, 2017, Nanjing, China.

l  Hyperspectral monitoring of crop growth: from canopies to organs. The International Conference on Intelligent Agriculture 2017. August, 2017, Changchun, China.

l  Assessing the spectral properties of rice organs with field-based hyperspectral imaging data. HZAU Plant Phenomics Forum. May, 2017, Wuhan, China.

l  Field phenotyping of rice and wheat crops with ground and unmanned aerial vehicle (UAV) based sensing technologies. Phenomatics Workshop. April, 2017. Shanghai, China.

l  Hyperspectral estimation of vegetation moisture at multiple scales. January 2016, Nanjing Institute of Geography & Limnology, CAS, Nanjing, China.

l  Quantitative remote sensing for retrieving vegetation parameters. May 2015, Sun Yat-Sen University, Guangzhou, China.

l  Detecting canopy water dynamics in nut tree orchards using multispectral and hyperspectral airborne data. June 2014, Nanjing University of Information Science and Technology, Nanjing, China.

l  Quantification of vegetation properties from remotely sensed data at leaf and canopy scales. May 2013, Nanjing University, Nanjing, China

l  A wavelet perspective on the quantification of vegetation water content from hyperspectral data at leaf and canopy scales. April 2013, Peking University, Beijing, China

l  Remotely sensed fuel moisture using leaf and imaging spectroscopy. ASPRS Northern California Region Technical Session “Remote Sensing of Fire and Ecosystem Impacts”, August 8th, 2012. McClellan, CA.

l  Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data. “Multiscale assessment of vegetation water content estimates and its impact on soil moisture for agricultural and natural vegetation” Project meeting, March 21, 2012, University of California, Davis, CA.

l  Estimating leaf fuel moisture content from reflectance spectra using continuous wavelet analysis. “Near Real Time Science Processing Algorithm for Live Fuel Moisture Content for the MODIS Direct Readout System” Project kick-off meeting, October 25, 2011, University of California, Davis, CA.

l  Continuous wavelet analysis for the detection of green attack due to mountain pine beetle infestation. Earth Observation Science Day, March 4, 2010, University of Alberta, Edmonton, Alberta, Canada.

l  Continuous wavelet analysis for the detection of green attack due to mountain pine beetle infestation. ATLAS Symposium, April 8-9, 2010, University of Alberta, Edmonton, Alberta, Canada.

 

AWARDS& HOUNORS

Chang Jiang Scholars, Ministry of Education (2024)

Shennong Youth Talent, Ministry of Agriculture and Rural Affairs (2022)

2021 RSE Best Reviewer, Elsevier (2021)

Youth Science and Technology Award, Crop Science Society of China (2019)

Youth Remote Sensing & GIS Science and Technology Award, Jiangsu Provincial Society of Remote Sensing & GIS (2018)

New Star in Research, College of Agriculture, Nanjing Agricultural University (2017)

Jiangsu Distinguished Professor (2014-2017, awarded by the Provincial Education Dept. of Jiangsu)

UC Davis Postdoctoral Scholars Association (PSA) Travel Grant (2013)               

Nominee for the Award for Excellence in Postdoctoral Research, UC Davis (2012)

Professional Development Grant, University of Alberta, Canada (2010)                 

J Gordin Kaplan Graduate Student Award, University of Alberta, Canada (2010)             

Visiting student scholarship, University of Genoa, Genoa, Italy (2006)                             

Outstanding graduate, Lanzhou University, Lanzhou, China (2003)

 

PROFESSIONAL ACTIVITIES

Senior Member, IEEE (2010-)

Chair, IEEE Geoscience & Remote Sensing Society Nanjing Chapter (2016-2020)

Editorial Board Member and Associate Editor, Precision Agriculture (2021/8-)

Associate Editor, IEEE J-STARS (2021/2-)

Associate Editor, CABI Agriculture & Biosciences (2021/6-)

Editorial Board, ISPRS International Journal of Geo-Information (2015/6-)

Guest Editor for the special issues “Recent Advances in Remote Sensing for Crop Growth Monitoring” in Remote Sensing (2014-2015) and “Optical Remote Sensing of Crop Growth and Health for Smart Farming” in IEEE-JSTARS (2020-2021)

Session Chair, 6th International Plant Phenotyping Symposium, October 22-26, 2019, Nanjing, China

Session Chair, 2nd Asia-Pacific Plant Phenotyping Conference. March 23-25, 2018. Nanjing, China.

Session Co-chair, 2018 Joint Annual Meeting for Jiangsu Provincial Society of Remote Sensing & GIS and Jiangsu Provincial Society of Geography. December 8, 2018. Nanjing, China.

Session Co-chair, International Conference on Carbon Cycle and Global Change, June 9-12, 2015, LinAn, Hangzhou, China

Program Co-chair & conference coordinator, International symposium on crop growth monitoring, September 13-16, 2014, Nanjing, China

Session Chair, 3rd Agro-Geoinformatics, August 11-14, 2014, Beijing, China

 

Reviewer for journals:

Computers and Electronics in Agriculture

Field Crops Research

European Journal of Agronomy

IEEE Transactions on Geoscience and Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

International Journal of Remote Sensing

ISPRS Journal of Photogrammetry and Remote Sensing

Nature Communications
Precision Agriculture

Remote Sensing of Environment

 

Graduate student advising

 

PhD: 10 completed, 9 ongoing

Master’s: 21 completed, 10 ongoing